1. Field
Embodiments of the present invention generally relate to speech recognition. More particular, embodiments of the present invention relate to the implementation of an acoustic modeling process on a dedicated processing unit.
2. Background
Real-time data pattern recognition is increasingly used to analyze data streams in electronic systems. On a vocabulary with over tens of thousands of words, speech recognition systems have achieved improved accuracy, making it an attractive feature for electronic systems. For example, speech recognition systems are increasingly common in consumer markets targeted to data pattern recognition applications such as, for example, the mobile device, server, automobile, and PC markets.
Despite the improved accuracy in speech recognition systems, significant computing resources are dedicated to the speech recognition process, in turn placing a significant load on computing systems such as, for example, multiuser/multiprogramming environments. Multiprogramming computing systems concurrently process data from various applications and, as a result, the load placed on these computing systems by the speech recognition process affects the speed at which the computing systems can process incoming voice signals as well as data from other applications. Further, for handheld devices that typically include limited memory resources (as compared to desktop computing systems), speech recognition applications not only place significant load on the handheld device's computing resources but also consume a significant portion of the handheld device's memory resources. The above speech recognition system issues of processing capability, speed, and memory resources are further exacerbated by the need to process incoming voice signals in real-time or substantially close to real-time.